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Trajectory outlier detection: New problems and solutions for smart cities

Djenouri, Youcef; Djenouri, Djamel; Chun-Wei Lin, Jerry

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Authors

Youcef Djenouri

Jerry Chun-Wei Lin



Abstract

This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five algorithms are proposed for the first problem by adapting DBSCAN, k nearest neighbors (kNN), and feature selection (FS). DBSCAN-GTO first applies DBSCAN to derive the micro clusters, which are considered as potential candidates. A pruning strategy based on density computation measure is then suggested to find the group of trajectory outliers. kNN-GTO recursively derives the trajectory candidates from the individual trajectory outliers and prunes them based on their density. The overall process is repeated for all individual trajectory outliers. FS-GTO considers the set of individual trajectory outliers as the set of all features, while the FS process is used to retrieve the group of trajectory outliers. The proposed algorithms are improved by incorporating ensemble learning and high-performance computing during the detection process. Moreover, we propose a general two-phase-based algorithm for detecting the deviation points, as well as a version for graphic processing units implementation using sliding windows. Experiments on a real trajectory dataset have been carried out to demonstrate the performance of the proposed approaches. The results show that they can efficiently identify useful patterns represented by group of trajectory outliers, deviation points, and that they outperform the baseline group detection algorithms.

Journal Article Type Article
Acceptance Date Dec 1, 2020
Online Publication Date Feb 1, 2021
Publication Date Feb 1, 2021
Deposit Date Apr 22, 2021
Publicly Available Date Jun 25, 2021
Journal ACM Transactions on Knowledge Discovery from Data
Print ISSN 1556-4681
Electronic ISSN 1556-472X
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Volume 15
Issue 2
Article Number 20
DOI https://doi.org/10.1145/3425867
Public URL https://uwe-repository.worktribe.com/output/7283549

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